Hasil untuk "q-fin.PM"

M. Caputo

Laboratory experiments and field observations indicate that tlie Q of many non ferromagnetic inorganic solids is almost frequency independent in the range 10' to 10~2 cps; although no single substance has been investigated over the entire frequency spectrum. One of the purposes of this investigation is to find the analytic expression of a linear dissipative mechanism whose Q is almost frequency independent over large frequency ranges. This will be obtained by introducing fractional derivatives in the stress strain relation. Since the aim of this research is to also contribute to elucidating the dissipating mechanism in the earth free modes, we shall treat the cases of dissipation in the free purely torsional modes of a shell and the purely radial vibration of a solid sphere. The theory is checked with the new values determined for the Q of the spheroidal free modes of the earth in the range between 10 and 5 minutes integrated with the Q of the Railegh waves in the range between 5 and 0.6 minutes. Another check of the theory is made with the experimental values of the Q of the longitudinal waves in an alluminimi rod, in the range between 10-5 and 10-3 seconds. In both clicks the theory represents the observed phenomena very satisfactory.

P. Goldreich, S. Soter

H. Huberti, W. Hayes

E. Ukkonen

We study approximate string matching in connection with two string distance functions that are computable in linear time. The first function is based on the so-called $q$-grams. An algorithm is given for the associated string matching problem that finds the locally best approximate occurences of pattern $P$, $|P|=m$, in text $T$, $|T|=n$, in time $O(n\log (m-q))$. The occurences with distance $\leq k$ can be found in time $O(n\log k)$. The other distance function is based on finding maximal common substrings and allows a form of approximate string matching in time $O(n)$. Both distances give a lower bound for the edit distance (in the unit cost model), which leads to fast hybrid algorithms for the edit distance based string matching.

W. Boden, R. O'rourke, M. Crawford et al.

K. Mackie, Y. Lai, R. Westenbroek et al.

Rat brain cannabinoid receptor (CB-1) was stably transfected into the murine tumor line AtT-20 to study its coupling to inwardly rectifying potassium currents (Kir) and high voltage-activated calcium currents (ICa). In cells expressing CB-1 (“A-2” cells), cannabinoid agonist potently and stereospecifically activated Kir via a pertussis toxin- sensitive G protein. ICa in A-2 cells was sensitive to dihydropyridines and omega CTX MVIIC, less so to omega CgTX GVIA and insensitive to omega Aga IVa. In CB-1 expressing cells, cannabinoid agonist inhibited only the omega CTX MVIIC-sensitive component of ICa. Inhibition of Q- type ICa was voltage dependent and PTX sensitive, thus similar in character to the well-studied modulation of N-type ICa. An endogenous cannabinoid, anandamide, activated Kir and inhibited ICa as efficaciously as potent cannabinoid agonist. Immunocytochemical studies with antibodies specific for class A, B, C, D, and E voltage-dependent calcium channel alpha 1 subunits revealed that AtT-20 cells express each of these major classes of alpha 1 subunit.

Michael Monoyios, Olivia Pricilia

We introduce a novel neural-network-based approach to learning the generating function $G(\cdot)$ of a functionally generated portfolio (FGP) from synthetic or real market data. In the neural network setting, the generating function is represented as $G_θ(\cdot)$, where $θ$ is an iterable neural network parameter vector, and $G_θ(\cdot)$ is trained to maximise investment return relative to the market portfolio. We compare the performance of the Neural FGP approach against classical FGP benchmarks. FGPs provide a robust alternative to classical portfolio optimisation by bypassing the need to estimate drifts or covariances. The neural FGP framework extends this by introducing flexibility in the design of the generating function, enabling it to learn from market dynamics while preserving self-financing and pathwise decomposition properties.

Zhengqian Luo, Min Zhou, Jian Weng et al.

We demonstrate a compact Q-switched dual-wavelength erbium-doped fiber (EDF) laser based on graphene as a saturable absorber (SA). By optically driven deposition of graphene on a fiber core, the SA is constructed and inserted into a diode-pumped EDF laser cavity. Also benefiting from the strong third-order optical nonlinearity of graphene to suppress the mode competition of EDF, a stable dual-wavelength Q-switching operation has been achieved using a two-reflection peak fiber Bragg grating as the external cavity mirror. The Q-switched EDF laser has a low pump threshold of 6.5 mW at 974 nm and a wide range of pulse-repetition rate from 3.3 to 65.9 kHz. The pulse duration and the pulse energy have been characterized. This is, to the best of our knowledge, the first demonstration of a graphene-based Q-switched laser.

Maysam Khodayari Gharanchaei, Reza Babazadeh

Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1,000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial crises: The Dot Com Bubble, Global Financial Crisis, and Covid-19 market downturn. Backtests leverage the CRSP dataset from January 1990 through December 2023. The results demonstrate that the proposed models consistently outperformed market excess returns across all periods. These findings suggest that the developed risk models can serve as valuable tools for asset managers, aiding in strategic decision-making and risk management in various economic conditions.

Hamidreza Maleki Almani, Foad Shokrollahi, Tommi Sottinen

We consider the jump-diffusion risky asset model and study its conditional prediction laws. Next, we explain the conditional least square hedging strategy and calculate its closed form for the jump-diffusion model, considering the Black-Scholes framework with interpretations related to investor priorities and transaction costs. We investigate the explicit form of this result for the particular case of the European call option under transaction costs and formulate recursive hedging strategies. Finally, we present a decision tree, table of values, and figures to support our results.

S. Chekanov, D. Krakauer, S. Magill et al.

J. Barry, J. Proops

Chi-Lin Li, Chung-Han Hsieh

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible window size adjustment based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive ten-year empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.

Magnus Bentinger, M. Tekle, G. Dallner

M. Littman

Seyed Mehdi Nasehi, Jonathan Sillito, F. Maurer et al.

Davide Lauria, W. Brent Lindquist, Stefan Mittnik et al.

ESG ratings provide a quantitative measure for socially responsible investment. We present a unified framework for incorporating numeric ESG ratings into dynamic pricing theory. Specifically, we introduce an ESG-valued return that is a linearly constrained transformation of financial return and ESG score. This leads to a more complex portfolio optimization problem in a space governed by reward, risk and ESG score. The framework preserves the traditional risk aversion parameter and introduces an ESG affinity parameter. We apply this framework to develop ESG-valued: portfolio optimization; capital market line; risk measures; option pricing; and the computation of shadow riskless rates.

Hyungbin Park, Heejun Yeo

This paper investigates dynamic and static fund separations and their stability for long-term optimal investments under three model classes. An investor maximizes the expected utility with constant relative risk aversion under an incomplete market consisting of a safe asset, several risky assets, and a single state variable. The state variables in two of the model classes follow a 3/2 process and an inverse Bessel process, respectively. The other market model has the partially observed state variable modeled as an Ornstein-Uhlenbeck state process. We show that the dynamic optimal portfolio of this utility maximization consists of m+3 portfolios: the safe asset, the myopic portfolio, the m time-independent portfolios, and the intertemporal portfolio. Over time, the intertemporal portfolio eventually vanishes, leading the dynamic portfolio to converge to m+2 portfolios, referred to as the static portfolio. We also prove that the convergence is stable under model parameter perturbations. In addition, sensitivities of the intertemporal portfolio with respect to small parameters perturbations also vanish in the long run. The convergence rate for the intertemporal portfolio and its sensitivities are computed explicitly for the presented models.

I. Aganj, C. Lenglet, G. Sapiro et al.

I. Bose, A. Leung

Identity theft has impaired e-commerce. To combat the crime, many identity theft countermeasures (ITC) have been proposed. As investments in ITC are substantial and the benefits of such investments are intangible, companies are often hesitant to adopt such measures. This was the motivation for this study of the impact of 526 ITC adoption announcements on short- and long-term market value. The event study shows that such announcements result in positive market return of about U.S. $583 million around the date of announcement. Calendar-time portfolio analysis (CPA) is used for the long-term impact analysis and shows that the adoption of ITC generates positive and significant average monthly return up to 1.5% with control of market risk factors in a year. Subsampling analysis and interaction analysis show that U.S. listing, early ITC adoption, and two- factor authentication may moderate the market value of ITC adopters differently. A number of robustness checks (e.g., Heckman model, cross-sectional regression on Tobin’s Q, firm-specific risk factor analysis, subsampling analysis by ICT development, and analysis of security statements in annual reports) are performed. The research provides quantitative evidence of financial gain resulting from adoption of ITC and aspires to raise ITC awareness among industrial practitioners.